Decision Tree-
The decision tree is made with root node and another is branches of that node with name leaf nodes. The outcome of that root is denoted with different possibilities output.
Classification By Decision Tree Induction-
The classification use a decision tree method for classifying data with different angles. In that classification tree the tree structure is defined as flowchart method, In that tree each internal node means known as leaf node denoted as test on the attribute and each branch represented and output of that test and each leaf node or terminal node holds a class label that class label is known factor. If we create a classification tree that not required the previous data or any domain knowledge about that industry because of that the decision tree induction is mainly used for classification method.
Advantages of Decision Tree-
- It does not required any domain knowledge about that industry.
- It is easy to maintain and easy to draw.
- Classification steps are easy to maintain in that decision tree.
Over Fitting and Tree Pruning-
If you work with classification and decision tree then over fitting problem arises. At the time of decision tree calculation if any over fitting problem arises then the accuracy of that classification is less. The over fitting problem means that the extra information is added in decision tree with adding extra node of that root. To avoid over fitting the pruning method is used for resolve that problem. Mainly two methods are used for over fitting problem-
- Pre-Pruning
- Post-Pruning
Pre-Pruning-
pre-Pruning checks firstly the extra node created. Checks or deciding no further split or partition tree without requirement. so the pre-Pruning method is worked successfully.
post-Pruning-
After creation tree free or delete unused node with classification rule then the post-Pruning method is used on that tree and deleting a nodes from that decision tree. If you done post-Pruning method then the accuracy of that tree is increased.